Description Usage Arguments Details Value See Also Examples
Slopes of columnwise logistic regressions of each column of a Filebacked Big Matrix, with some other associated statistics. Covariates can be added to correct for confounders.
1 2 3 4 5 6 7 8 9 10  big_univLogReg(
X,
y01.train,
ind.train = rows_along(X),
ind.col = cols_along(X),
covar.train = NULL,
tol = 1e08,
maxiter = 20,
ncores = 1
)

X 
An object of class FBM. 
y01.train 
Vector of responses, corresponding to 
ind.train 
An optional vector of the row indices that are used, for the training part. If not specified, all rows are used. Don't use negative indices. 
ind.col 
An optional vector of the column indices that are used. If not specified, all columns are used. Don't use negative indices. 
covar.train 
Matrix of covariables to be added in each model to correct
for confounders (e.g. the scores of PCA), corresponding to 
tol 
Relative tolerance to assess convergence of the coefficient.
Default is 
maxiter 
Maximum number of iterations before giving up.
Default is 
ncores 
Number of cores used. Default doesn't use parallelism. You may use nb_cores. 
If convergence is not reached by the main algorithm for some columns,
the corresponding niter
element is set to NA
and a message is given.
Then, glm is used instead for the corresponding column.
If it can't converge either, all corresponding estimations are set to NA
.
A data.frame with 4 elements:
the slopes of each regression,
the standard errors of each slope,
the number of iteration for each slope. If is NA
, this means that the
algorithm didn't converge, and glm was used instead.
the zscores associated with each slope.
This is also an object of class mhtest
. See methods(class = "mhtest")
.
glm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  set.seed(1)
X < big_attachExtdata()
n < nrow(X)
y01 < sample(0:1, size = n, replace = TRUE)
covar < matrix(rnorm(n * 3), n)
X1 < X[, 1] # only first column of the Filebacked Big Matrix
# Without covar
test < big_univLogReg(X, y01)
## new class `mhtest`
class(test)
attr(test, "transfo")
attr(test, "predict")
## plot results
plot(test)
plot(test, type = "Volcano")
## To get pvalues associated with the test
test$p.value < predict(test, log10 = FALSE)
str(test)
summary(glm(y01 ~ X1, family = "binomial"))$coefficients[2, ]
# With all data
str(big_univLogReg(X, y01, covar.train = covar))
summary(glm(y01 ~ X1 + covar, family = "binomial"))$coefficients[2, ]
# With only half of the data
ind.train < sort(sample(n, n/2))
str(big_univLogReg(X, y01[ind.train],
covar.train = covar[ind.train, ],
ind.train = ind.train))
summary(glm(y01 ~ X1 + covar, family = "binomial",
subset = ind.train))$coefficients[2, ]

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.